Thursday, December 29, 2016

Another year is almost over. As the last post of this year I decided to share with you the 15 most popular posts in 2016. I just used Google Analytics to grab this info, I excluded the home page from the top 15. Four of these posts were written in 2006 and one was written in 2005

Tuesday, December 27, 2016

You didn't go to the PASS summit this year, but you would still want to watch the sessions? There is a way now, you can buy the USB stick with all the sessions, you can also download the sessions you are interested in. The passboutique site has the details, They also have a sale going on at the moment.

So if you got a bunch of money over the holidays, this would be a great investment.....

Saturday, December 17, 2016

A nice holiday present for you all has just arrived: SQL Server next version Community Technology Preview 1.1

Here is what is new in terms of the SQL Engine

Language and performance enhancements to natively compiled T-SQL modules, including support for OPENJSON, FOR JSON, JSON built ins as well as memory-optimized tables support for computed columns.

Improved the performance of updates to non-clustered columnstore indexes in the case when the row is in the delta store.

Batch mode queries now support “memory grant feedback loops,” which learn from memory used during query execution and adjusts on subsequent query executions; this can allow more queries to run on systems that are otherwise blocking on memory.

New T-SQL language features:

Introducing three new string functions: TRIM, CONCAT_WS, and TRANSLATE

BULK IMPORT supports CSV format and Azure Blob Storage as file source

STRING_AGG supports WITHIN GROUP (ORDER BY)s

TRIM

So finally we can write the following instead of doing LTRIM and RTRIM

SELECTTRIM( ' NoSPaces ') ASResult;

That will return just the value NoSpaces

You can also specify what characters to trim

TRANSLATE

This acts like a bunch of replace statements, instead of REPLACE(REPLACE(REPLACE(REPLACE(SomeVal,'[','('),']',,')'),'{','('),'}',,')') you can do the following which is much cleaner

Python is one of the most popular and fastest-growing languages used today. Pyodbc (Python-SQL Server Connector) is an open source Python module maintained by Michael Kleehammer that uses ODBC Drivers to connect to SQL Server. It allows you to connect from the platform of your choice to SQL Server on-premises and in the cloud. Pyodbc is also cross platform and can be installed using pip.

We recently announced SQL Server v.Next CTP1 on Linux and Windows, which brings the power of SQL Server to both Windows and — for the first time ever — Linux. You can now connect your applications to SQL Server running on Linux, Windows and macOS (with Docker).

For our Python developers, we have a few updates that will improve Python connectivity with SQL Server. Pyodbc is now:

With the recent announcement of SQL Server 2016 SP1, we announced the consistent programmability experience for developers and ISVs, who can now maintain a single code base and build intelligent database applications which scale across all the editions of SQL Server. The processor, memory and database size limits does not change and remain as–in all editions as documented in the SQL Server editions page. We have made the following changes in our documentation to accurately reflect the memory limits on lower editions of SQL Server. This blog post is intended to clarify and provide more information on the memory limits starting with SQL Server 2016 SP1 on Standard, Web and Express Editions of SQL Server.

SQL Server Management Studio 17.o (the next major update of SSMS, currently available as a Release Candidate) introduces two important capabilities for Always Encrypted:

Ability to insert into, update and filter by values stored in encrypted columns from a Query Editor window.

The new online encryption algorithm, exposed in the Set-SqlColumnEncryption PowerShell cmdlet, which makes tables available for both reads and writes during the initial encryption and column encryption key rotation.

While we often worry about sophisticated digital attacks, the most common attacks for accessing news organizations’ accounts depend on only a few simple weaknesses. These weaknesses are usually a combination of predictable passwords, phishing emails designed to steal login credentials, as well as malicious file attachments in email and elsewhere. While the attacks are simple, so are the defenses. This collection of resources and learning materials will walk you through practices recommended by security specialists for defending your newsroom against common attacks on your accounts.

Making SQL Server run on Linux involves introducing what is known as a Platform Abstraction Layer (“PAL”) into SQL Server. This layer is used to align all operating system or platform specific code in one place and allow the rest of the codebase to stay operating system agnostic. Because of SQL Server’s long history on a single operating system, Windows, it never needed a PAL. In fact, the SQL Server database engine codebase has many references to libraries that are popular on Windows to provide various functionality. In bringing SQL Server to Linux, we set strict requirements for ourselves to bring the full functional, performance, and scale value of the SQL Server RDBMS to Linux. This includes the ability for an application that works great on SQL Server on Windows to work equally great against SQL Server on Linux. Given these requirements and the fact that the existing SQL Server OS dependencies would make it very hard to provide a highly capable version of SQL Server outside of Windows in reasonable time it was decided to marry parts of the Microsoft Research (MSR) project Drawbridge with SQL Server’s existing platform layer SQL Server Operating System (SOS) to create what we call the SQLPAL. The Drawbridge project provided an abstraction between the underlying operating system and the application for the purposes of secure containers and SOS provided robust memory management, thread scheduling, and IO services. Creating SQLPAL enabled the existing Windows dependencies to be used on Linux with the help of parts of the Drawbridge design focused on OS abstraction while leaving the key OS services to SOS. We are also changing the SQL Server database engine code to by-pass the Windows libraries and call directly into SQLPAL for resource intensive functionality.

Friday, December 09, 2016

One of our database on the development went in suspect mode today. This database was the default for a bunch of logins. These people could not login now. Someone needed to use a different database but he couldn’t login because the database that was in suspect mode was the default database for the login he was using.

I told this person to click on the Options button in the connection dialog and specify another database. I guess there was an misunderstanding because this person couldn’t get it to work. This means it is time for a blog post.

Let's take a look how this all works

Here is a script that will create 2 databases

CREATEDATABASE Good
GOCREATEDATABASE OopsBad
GO

Now create a new login named TestLogin with a password of Test. We are also adding the login we just created to the OopsBad database and we will make the login part of the db_owner role

SQL Server 2016 SP1 adds a significant new performance feature, the ability to accelerate transaction commit times (latency) by up to 2-4X, when employing Storage Class Memory (NVDIMM-N nonvolatile storage). This scenario is also referred to as “persistent log buffer” as explained below.

This enhancement is especially valuable for workloads which require high frequency, low latency update transactions. These app patterns are common in the finance/trading industry as well as online betting and some process control applications.

As we all wind down for the 2016 holiday season, we want to give the SQL Server community a holiday gift to say ‘thank you’ for all your support during 2016, and what better gift than more free content?!

As many of you know, I publish a bi-weekly newsletter to more than 13,500 subscribers that contains an editorial on a SQL Server topic, a demo video, and a book review of my most recently completed book. We’re making all the 2015 demo videos available so everyone can watch them – 25 videos in all, mostly in WMV format. I did the same thing the last few years for the 2014 videos, 2013 videos, 2012 videos, and 2011 videos.

One of the most important actions when a performance issue hits, is to get precise understanding on the workload that’s executing and how resource usage is being driven. The actual execution plan is an invaluable tool for this purpose.

Query completion is a prerequisite for the availability of an actual query plan, but with LQS (Live Query Statistics), you can already get information about in-flight query executions (see this blog post), which is especially useful for long running queries, and queries that run indefinitely and never finish.

One of my most enduring and popular presentations is called End-to-End Troubleshooting Checklist for Microsoft SQL Server”. In this presentation, I take you through my six-step checklist from detection of a performance issue on SQL Server through identification of the root cause to remediation and finally post-mortem steps to ensure the problem is prevented (or at least detected immediately) in the future.

Over the years, I’ve had many inquiries about the slides, T-SQL scripts, and additional troubleshooting information. I’m happy to report that I’ve finally collated all of the associated content from that presentation!

So what is a Selective XML index? It’s an index! For XML! Where you pick the parts of the XML to index. Other XML indexes are rather all or nothing, and can end up being pretty huge, depending on the size of your documents. I think they’re at least size of data, if I recall correctly. Let’s take a look at some examples.

The sp_WhatsupQueryStore Stored Procedure is a Microsoft SQL Server Stored Procedure that retrieves all kinds of information from the Query Store. By running the script on this website the sp_WhatsupQueryStore Stored Procedure gets installed in the "master" database of your SQL Server Instance.

Columnstore index is the preferred technology to run analytics queries in Azure SQL Databases. We recently announced general availability if In-Memory technologies for all Premium databases. Similar to In-Memory OLTP, the columnstore index technology is available in premium databases.

The columnstore technology is available in two flavors; clustered columnstore index (CCI) for DataMart analytics workloads and nonclustered columnstore index (NCCI) to run analytics queries on operational (i.e. OLTP) workload. Please refer to NCCI vs CCI for the differences between these two flavors of columnstore indexes. The columnstore index can speed up the performance of analytics queries up to 100x while significantly reducing the storage footprint. The data compression achieved depends on the schema and the data, but we see around 10x data compression on average when compared to rowstore with no compression. This blog will focus on Analytic workloads using CCI but cover NCCI in a future blog.

Java continues to be one of the most widely used programming languages for a variety of application scenarios and industries. The Microsoft JDBC Driver for SQL Server is used to connect Java applications to SQL Server, whether SQL Server is hosted in the cloud or on-premises, or provided as a platform-as-a-service.

With the release of SQL Server v.Next public preview on Linux and Windows, the ability to connect to SQL Server on Linux, Windows, Docker or macOS (via Docker) makes cross-platform support for all connectors, including the JDBC driver, even more important. To enable Java developers to use the newest SQL Server features, we have been updating the JDBC driver with client-side support for new features, including Always Encrypted and Azure Active Directory Authentication.

This release includes a brand new R package for machine learning: MicrosoftML. This package provides state-of-the-art, fast and scalable machine learning algorithms for common data science tasks including featurization, classification and regression. Some of the functions provided include:

We are working on significantly updating the Management Pack for Azure SQL Database.

This release will bring support for Elastics Pools and Azure AD Authentication among other new features. We are also working on handling monitoring of larger number of databases. We are expecting to improve the scale by the time we RTM. Here are some numbers to give you an idea for this public preview:

With the introduction of the Temporal feature in SQL 2016 and Azure SQL Database, there is an ability to time travel through the state of data as it was at any given point of time. Alongside In-Memory OLTP, Temporal on memory optimized tables allows you to harness the speed of In-Memory OLTP, and gives you the same ability to track history and audit every change made to a record. Temporal added to memory optimized tables also allows you to maintain a “smaller” memory optimized tables and thereby a smaller memory footprint by deleting data that isn’t “hot” anymore from the current memory optimized table, which in turn moves it to the history table without having an external archival process to do that.

When memory optimized and temporal tables are combined, an internal memory optimized table is created in addition to the history table, as depicted in the diagram below. Data is flushed asynchronously from the internal in-memory History table to the disk based history table. The flush interval isn’t currently configurable. Data is flushed when the internal table reaches 8% of the memory consumed by the current table, OR you can flush it manually by executing the procedure sys.sp_xtp_flush_temporal_history. The internal memory optimized table is created with the same column definitions as the current in-memory table, but with a single index.

As requested by the community to complete the per-operator information, starting with SQL Server 2016 SP1 we are now exposing memory grant per grant iterator (such as Sorts and Hash Matches). These give you added insight into memory grants, and how overall memory usage is driven throughout execution.

All the Internet giants, including Microsoft, now supplement their CPUs with graphics processing units, chips designed to render images for games and other highly visual applications. When these companies train their neural networks to, for example, recognize faces in photos—feeding in millions and millions of pictures—GPUs handle much of the calculation.

Some giants like Microsoft are also using alternative silicon to execute their neural networks after training. And even though it’s crazily expensive to custom-build chips, Google has gone so far as to design its own processor for executing neural nets, the tensor processing unit.

Lock Pages in Memory and Instant File Initialization privileges are couple of configuration settings which every DBA, SQL Server consultant or enthusiast have it in their checklist to ensure they see a predictable performance for their SQL Server instance. While Lock Pages in Memory privilege information is logged in SQL Error log, Instant File initialization information was first introduced in SQL Errorlog starting SQL Server 2016 RTM and later added to SQL Server 2014 with SP2.

When you are managing, administering or monitoring large deployment of SQL Servers, it is still cumbersome to programmatically query SQL Error log to check if these permissions are enabled for the SQL Server service account. With SQL Server 2016 SP1, we have added new columns in the DMV which now makes it easy to develop scripts to programmatically query and report whether Lock Pages in Memory and instant file initialization privileges are enabled on a given instance of SQL Server.

SQL Server uses memory to store in-transit rows for hash join and sort operations. When a query execution plan is compiled for a statement, SQL Server estimates both the minimum required memory needed for execution and the ideal memory grant size needed to have all rows in memory. This memory grant size is based on the estimated number of rows for the operator and the associated average row size. If the cardinality estimates are inaccurate, performance can suffer:

For cardinality under-estimates, the memory grant can end up being too small and the rows then spill to disk, causing significant performance degradation compared to a fully memory-resident equivalent.

For cardinality over-estimates, the memory grant can be too large and the memory goes to waste. Concurrency can be impacted because the query may wait in a queue until enough memory becomes available, even though the query only ends up using a small portion of the granted memory.

With SQL Server on Linux, Microsoft brings SQL Server’s core relational database engine to the growing enterprise Linux ecosystem. Both High Availability and Disaster Recovery (HADR) and security are aspects of SQL Server that are critically important for enterprises. This article highlights the HADR and security solutions for SQL Server on Linux that are available today, as well as the roadmap for what’s coming soon.

Today, developers can use SQL Server in a variety of environments including on-premises, in datacenters, in virtual machines, in clouds such as Azure, AWS and Google, and also as a Platform as a Service (PaaS) offering with Azure SQL Database and Azure SQL Data Warehouse.

We recently announced SQL Server v.Next CTP1 on Linux and Windows, which brings the power of SQL Server to both Windows — and for the first time ever — Linux. Developers can now create applications with SQL Server on Linux, Windows, Docker, or macOS (via Docker) and then deploy to Linux, Windows, or Docker, on-premises or in the cloud.

As part of this announcement, we have released new SQL tools and also updated existing SQL tools. Developers can use these tools to connect to and work with SQL running anywhere, including SQL Server on Linux, Windows or Docker.

I sense many useless updates in you... Useless updates lead to fragmentation... Fragmentation leads to downtime...Downtime leads to suffering..Fragmentation is the path to the darkside.. DBCC INDEXDEFRAG and DBCC DBREINDEX are the force...May the force be with you"